Privacy-Preserving Imaging: Algorithms and Systems Resilient to Cyber-Attacks

This Research Topic investigates privacy-preserving imaging methods and end-to-end system designs that remain robust under cyber threats. As imaging pipelines increasingly integrate AI-driven analytics, cloud-edge processing, and distributed sensors, the attack surface expands—exposing sensitive visual data to leakage, tampering, model inversion, and adversarial manipulation. Ensuring confidentiality, integrity, and availability of imaging data and models is essential for trustworthy applications in healthcare, autonomous systems, remote sensing, and public safety.

Original research, reviews, and case studies are welcome on algorithms, protocols, and architectures that protect visual information while sustaining utility and performance. Submissions may cover secure and private image acquisition, resilient coding and transmission, adversarially robust learning, and defense-in-depth system engineering. This Research Topic aims to accelerate advances that balance privacy, security, and accuracy, enabling dependable imaging across hostile or resource-constrained environments.

To gather further insights in this evolving area, articles are invited — but not limited to — the following themes:

- Privacy-preserving imaging pipelines (e.g., federated, split, or on-device learning for images and video)
- Differential privacy, homomorphic encryption, and secure multi-party computation for image analysis
- Robust image/video coding and transmission with integrated authentication and watermarking
- Adversarial robustness for imaging AI (detection, defense, and certified guarantees)
- Secure sensor and edge architectures for distributed and IoT-enabled imaging networks
- Detection and mitigation of data/model poisoning, backdoors, and model extraction in imaging systems
- Real-time integrity verification, provenance tracking, and deepfake/forgery detection
- Risk assessment, benchmarking, and red-teaming methodologies for imaging cybersecurity
- Regulatory, ethical, and human-centered considerations for privacy-preserving visual analytics

By uniting advances in imaging, cryptography, machine learning security, and systems engineering, this collection seeks to chart a path toward private, resilient, and trustworthy imaging technologies that can withstand cyber-attacks while serving critical societal needs.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: privacy-preserving imaging, adversarial robustness, secure image coding, homomorphic encryption, federated learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.